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This review summarizes key recent developments relevant to the pathologic diagnosis of lung neuroendocrine neoplasms, including carcinoids, small cell lung carcinoma (SCLC), and large cell neuroendocrine carcinoma (LCNEC). Covered are recent insights into the biological subtypes within each main tumor type, progress in pathological diagnosis and immunohistochemical markers, and persistent challenging areas. Highlighted topics include highly proliferative carcinoids and their distinction from small cell and large cell neuroendocrine carcinomas (NECs), the evolving role of Ki67, the update on the differential diagnosis of NEC to include thoracic SMARCA4-deficient undifferentiated tumors, the recent data on SCLC transcriptional subtypes with the emergence of POU2F3 as a novel marker for the diagnosis of SCLC with low/negative expression of standard neuroendocrine markers, and the update on the diagnosis of LCNEC, particularly in biopsies. There has been remarkable recent progress in the understanding of the genetic and expression-based profiles within each type of lung neuroendocrine neoplasm, and it is hoped that these insights will enable the development of novel diagnostic, prognostic, and predictive biomarkers to aid in the pathologic assessment of these tumors in the future.

Key Points Oncogene-addicted subtypes of non-small-cell lung cancer (NSCLC), with dramatic responses to tyrosine kinase inhibitors (TKIs) are well recognized, with EGFR mutant and ALK rearrengement being two prominent examples Acquired resistance after initial clinical benefit inevitably occurs, usually within 1–2 years of starting therapy Acquired resistance can occur through failure of drug delivery to the target, as in isolated central nervous system (CNS) progression, or by selection of biological variants during TKI exposure Treatment approaches to acquired resistance include use of local ablative therapies to sites of progression and continuation of TKIs, cytotoxic chemotherapy or, if available, change in relevant targeted therapy Clinical trials of acquired resistance in the future may have to consider the importance of the CNS as a sanctuary site Trials of acquired resistance should consider the re-emergence of disease sensitive to the original TKI if there is an intervening period when the TKI-specific resistance selection pressure is relaxed This Review explores breakthroughs in our understanding and treatment of acquired resistance to tyrosine kinase inhibitors in key molecular subtypes of non-small-cell lung cancer, which may be relevant across multiple different solid malignancies with oncogene-addicted subtypes. The potential of a number of clinical approaches to treat acquired resistance, from new drugs or drug combinations through to the use of more traditional therapies such as radiation or cytotoxic chemotherapy, are highlighted, and the implications for major changes in conducting clinical research in this setting are discussed. The use of advanced molecular profiling to direct the use of targeted therapy, such as tyrosine kinase inhibitors (TKIs) for patients with advanced-stage non-small-cell lung cancer (NSCLC), has revolutionized the treatment of this disease. However, acquired resistance, defined as progression after initial benefit, to targeted therapies inevitably occurs. This Review explores breakthroughs in the understanding and treatment of acquired resistance in NSCLC, focusing on EGFR mutant and ALK rearrangement-positive disease, which may be relevant across multiple different solid malignancies with oncogene-addicted subtypes. Mechanisms of acquired resistance may be pharmacological (that is, failure of delivery of the drug to its target) or biological, resulting from evolutionary selection on molecularly diverse tumours. A number of clinical approaches can maintain control of the disease in the acquired resistance setting, including the use of radiation to treat isolated areas of progression and adding or switching to cytotoxic chemotherapy. Furthermore, novel approaches that have already proven successful include the development of second-generation and third-generation inhibitors and the combination of some of these inhibitors with antibodies directed against the same target. With our increased understanding of the spectrum of acquired resistance, major changes in how we conduct clinical research in this setting are now underway.

The clinical characteristics of lung cancer in patients with idiopathic pulmonary fibrosis (IPF) differ from those of lung cancer in patients without IPF. Thus, we aimed to evaluate the impact of IPF on the clinical course of patients with lung cancer. Clinical data of IPF patients with lung cancer (n = 122) were compared with those of patients with lung cancer without IPF (n = 488) matched by age, sex, histopathology, stage, and date of diagnosis of lung cancer. The median follow-up period after diagnosis of lung cancer was 16 months. Among patients with IPF, the mean age was 68 years, 95.9% were male, 93.2% were ever-smokers, and squamous cell carcinoma was the most common cancer type (48.4%). The IPF group had poorer lung function and lower lobe predominance of lung cancer than the no-IPF group. The IPF group showed a poorer prognosis than the no-IPF group (5-year survival rate: 14.5% vs. 30.1%, respectively; P  < 0.001), even after adjusting for lung function and regardless of the treatment method. Among patients with IPF, 16.8% experienced acute exacerbation within 1 month after treatment of lung cancer. The treatment outcome of patients with lung cancer and IPF was generally unfavorable, and acute exacerbation triggered by treatment frequently occurred.

The complex polygenic nature of lung cancer is not fully characterized. Our study seeks to identify novel phenotypes associated with lung cancer using cross-trait linkage disequilibrium score regression (LDSR). We measured pairwise genetic correlation (r g ) and SNP heritability (h 2 ) between 347 traits and lung cancer risk using genome-wide association study summary statistics from the UKBB and OncoArray consortium. Further, we conducted analysis after removing genomic regions previously associated with smoking behaviors to mitigate potential confounding effects. We found significant negative genetic correlations between lung cancer risk and dietary behaviors, fitness metrics, educational attainment, and other psychosocial traits. Alcohol taken with meals (r g  = − 0.41, h 2  = 0.10, p = 1.33 × 10 –16 ), increased fluid intelligence scores (r g  = − 0.25, h 2  = 0.22, p = 4.54 × 10 –8 ), and the age at which full time education was completed (r g  = − 0.45, h 2  = 0.11, p = 1.24 × 10 –20 ) demonstrated negative genetic correlation with lung cancer susceptibility. The body mass index was positively correlated with lung cancer risk (r g  = 0.20, h 2  = 0.25, p = 2.61 × 10 –9 ). This analysis reveals shared genetic architecture between several traits and lung cancer predisposition. Future work should test for causal relationships and investigate common underlying genetic mechanisms across these genetically correlated traits.

A model’s ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a clinically-oriented approach to uncertainty quantification for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without uncertainty, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that uncertainty thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts. Safe clinical deployment of deep learning models for digital pathology requires reliable estimates of predictive uncertainty. Here the authors describe an algorithm for quantifying whole-slide image uncertainty, demonstrating their approach with models trained to distinguish lung cancer subtypes.

The discoveries of EGFR mutations and ALK rearrangements as actionable oncogenic drivers in non-small-cell lung cancer (NSCLC) has propelled a biomarker-directed treatment paradigm for patients with advanced-stage disease. Numerous EGFR and ALK tyrosine kinase inhibitors (TKIs) with demonstrated efficacy in patients with EGFR-mutant and ALK-rearranged NSCLCs have been developed, culminating in the availability of the highly effective third-generation TKIs osimertinib and lorlatinib, respectively. Despite their marked efficacy, resistance to these agents remains an unsolved fundamental challenge. Both ‘on-target’ mechanisms (largely mediated by acquired resistance mutations in the kinase domains of EGFR or ALK) and ‘off-target’ mechanisms of resistance (mediated by non-target kinase alterations such as bypass signalling activation or phenotypic transformation) have been identified in patients with disease progression on osimertinib or lorlatinib. A growing understanding of the biology and spectrum of these mechanisms of resistance has already begun to inform the development of more effective therapeutic strategies. In this Review, we discuss the development of third-generation EGFR and ALK inhibitors, predominant mechanisms of resistance, and approaches to tackling resistance in the clinic, ranging from novel fourth-generation TKIs to combination regimens and other investigational therapies.Patients with non-small-cell lung cancers (NSCLCs) harbouring oncogenic EGFR or ALK alterations can benefit from therapies targeting these alterations, although acquired resistance to these agents is common. Third-generation inhibitors have extended the response durations of many patients with NSCLCs harbouring these alterations, albeit with differing patterns of resistance to those associated with earlier-generation agents. Here, the authors describe the mechanisms of acquired resistance to third-generation EGFR and ALK inhibitors and provide insights into future research directions in this area.

Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients’ prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma ( P <0.003) or squamous cell carcinoma ( P =0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort ( P <0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs. Diagnosis of lung cancer through manual histopathology evaluation is insufficient to predict patient survival. Here, the authors use computerized image processing to identify diagnostically relevant image features and use these features to distinguish lung cancer patients with different prognoses.

Small-cell lung cancer (SCLC) represents about 15% of all lung cancers and is marked by an exceptionally high proliferative rate, strong predilection for early metastasis and poor prognosis. SCLC is strongly associated with exposure to tobacco carcinogens. Most patients have metastatic disease at diagnosis, with only one-third having earlier-stage disease that is amenable to potentially curative multimodality therapy. Genomic profiling of SCLC reveals extensive chromosomal rearrangements and a high mutation burden, almost always including functional inactivation of the tumour suppressor genes TP53 and RB1 . Analyses of both human SCLC and murine models have defined subtypes of disease based on the relative expression of dominant transcriptional regulators and have also revealed substantial intratumoural heterogeneity. Aspects of this heterogeneity have been implicated in tumour evolution, metastasis and acquired therapeutic resistance. Although clinical progress in SCLC treatment has been notoriously slow, a better understanding of the biology of disease has uncovered novel vulnerabilities that might be amenable to targeted therapeutic approaches. The recent introduction of immune checkpoint blockade into the treatment of patients with SCLC is offering new hope, with a small subset of patients deriving prolonged benefit. Strategies to direct targeted therapies to those patients who are most likely to respond and to extend the durable benefit of effective antitumour immunity to a greater fraction of patients are urgently needed and are now being actively explored. Small-cell lung cancer is a rapidly proliferating cancer with a poor prognosis and is strongly associated with exposure to tobacco carcinogens. This Primer describes the epidemiology, pathogenesis and diagnosis of the disease as well as the current management approaches and new therapies that might improve outcomes.

Small cell lung cancer (SCLC) comprises 14% of all lung cancers, and >30 000 new cases are diagnosed per year in the United States. SCLC is one of the most distinctive malignancies in the entire field of oncology with characteristic clinical properties, responsiveness to specific chemotherapy, genetic features and a highly reliable pathological diagnosis. SCLC is defined by light microscopy, and the most important stain is a good-quality hematoxylin and eosin (H&E)-stained section. The vast majority of cases can be diagnosed on H&E alone; however, in problem cases, immunohistochemistry can be very helpful in making the distinction from other tumors. Cytology is also a powerful tool, often being more definitive than small biopsies with scant tumor cells, crush artifact and/or necrosis. As virtually all SCLCs present in advanced stages, most patients are diagnosed based on small biopsy and cytology specimens. Historically, there has been significant evolution in the histological subclassification of SCLC dating from 1962 when Kreyberg proposed the oat cell and polygonal cell types. The current subclassification recognizes only two subtypes: pure SCLC and combined SCLC. Pathologists need to do their best to make a diagnosis of SCLC or other histological types of lung cancer and this can be achieved in most cases. This review will address some of the diagnostic problems that occur in the minority of cases and outline practical ways to address them. Brief reference will be made to other neuroendocrine lung tumors with an overview of the molecular pathogenesis of this spectrum of tumors.

Adoptive cell therapy using tumor-infiltrating lymphocytes (TILs) has shown activity in melanoma, but has not been previously evaluated in metastatic non-small cell lung cancer. We conducted a single-arm open-label phase 1 trial ( NCT03215810 ) of TILs administered with nivolumab in 20 patients with advanced non-small cell lung cancer following initial progression on nivolumab monotherapy. The primary end point was safety and secondary end points included objective response rate, duration of response and T cell persistence. Autologous TILs were expanded ex vivo from minced tumors cultured with interleukin-2. Patients received cyclophosphamide and fludarabine lymphodepletion, TIL infusion and interleukin-2, followed by maintenance nivolumab. The end point of safety was met according to the prespecified criteria of ≤17% rate of severe toxicity (95% confidence interval, 3–29%). Of 13 evaluable patients, 3 had confirmed responses and 11 had reduction in tumor burden, with a median best change of 35%. Two patients achieved complete responses that were ongoing 1.5 years later. In exploratory analyses, we found T cells recognizing multiple types of cancer mutations were detected after TIL treatment and were enriched in responding patients. Neoantigen-reactive T cell clonotypes increased and persisted in peripheral blood after treatment. Cell therapy with autologous TILs is generally safe and clinically active and may constitute a new treatment strategy in metastatic lung cancer. Adoptive cell therapy with tumor-infiltrating lymphocytes in metastatic lung cancer patients is safe and elicits antitumor activity, including ongoing complete responses, in association with polyclonal T cell responses against tumor antigens.